Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Requirement already satisfied: matplotlib==2.0.2 in /opt/conda/lib/python3.6/site-packages
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
You are using pip version 9.0.1, however version 18.0 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fc534be62b0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fc534ac77f0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), 'real_input')
    z_input = tf.placeholder(tf.float32, (None, z_dim), 'z_input')
    lr = tf.placeholder(tf.float32, name='lr')

    return real_input, z_input, lr

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
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==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.01
        keep_prob = 0.9
        
        l1 = tf.layers.conv2d(images, 64, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        l1 = tf.nn.dropout(l1, keep_prob)
        r1 = tf.maximum(alpha * l1, l1)
        
        l2 = tf.layers.conv2d(r1, 128, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        l2 = tf.nn.dropout(l2, keep_prob)
        bn2 = tf.layers.batch_normalization(l2, training=True)
        r2 = tf.maximum(alpha * bn2, bn2)
        
        l3 = tf.layers.conv2d(r2, 256, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        l3 = tf.nn.dropout(l3, keep_prob)
        bn3 = tf.layers.batch_normalization(l3, training=True)
        r3 = tf.maximum(alpha * bn3, bn3)
        
        flat = tf.reshape(r3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.01
        keep_prob = 0.9
        
        l1 = tf.layers.dense(z, 7*7*512)
        l1 = tf.reshape(l1, (-1, 7, 7, 512))
        l1 = tf.nn.dropout(l1, keep_prob)
        bn1 = tf.layers.batch_normalization(l1, training=is_train)
        r1 = tf.maximum(alpha * bn1, bn1)
        
        l2 = tf.layers.conv2d_transpose(r1, 256, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        l2 = tf.nn.dropout(l2, keep_prob)
        bn2 = tf.layers.batch_normalization(l2, training=is_train)
        r2 = tf.maximum(alpha * bn2, bn2)
        
        l3 = tf.layers.conv2d_transpose(r2, 128, 5, 2, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        l3 = tf.nn.dropout(l3, keep_prob)
        bn3 = tf.layers.batch_normalization(l3, training=is_train)
        r3 = tf.maximum(alpha * bn3, bn3)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(r3, out_channel_dim, 5, 1, 'same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        out = tf.tanh(logits)
        
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1

    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1-smooth)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    print_counter = 100
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # normalize the image input from -0.5 to 0.5 to -1 to 1
                batch_images *= 2
                
                # optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % print_counter == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(i + 1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % print_counter == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [13]:
batch_size = 32
z_dim = 64
learning_rate = 0.0006
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 100... Discriminator Loss: 0.3382... Generator Loss: 7.0680
Epoch 1/2... Batch 200... Discriminator Loss: 0.3589... Generator Loss: 7.5587
Epoch 1/2... Batch 300... Discriminator Loss: 0.3299... Generator Loss: 8.5461
Epoch 1/2... Batch 400... Discriminator Loss: 0.3316... Generator Loss: 9.4211
Epoch 1/2... Batch 500... Discriminator Loss: 1.2462... Generator Loss: 1.0364
Epoch 1/2... Batch 600... Discriminator Loss: 1.3424... Generator Loss: 0.7878
Epoch 1/2... Batch 700... Discriminator Loss: 1.5979... Generator Loss: 1.6865
Epoch 1/2... Batch 800... Discriminator Loss: 1.3481... Generator Loss: 0.9982
Epoch 1/2... Batch 900... Discriminator Loss: 1.2504... Generator Loss: 0.8458
Epoch 1/2... Batch 1000... Discriminator Loss: 1.1997... Generator Loss: 1.1585
Epoch 1/2... Batch 1100... Discriminator Loss: 1.1071... Generator Loss: 0.9048
Epoch 1/2... Batch 1200... Discriminator Loss: 1.2277... Generator Loss: 0.9479
Epoch 1/2... Batch 1300... Discriminator Loss: 1.2638... Generator Loss: 1.2944
Epoch 1/2... Batch 1400... Discriminator Loss: 1.2118... Generator Loss: 1.0554
Epoch 1/2... Batch 1500... Discriminator Loss: 1.0833... Generator Loss: 1.0217
Epoch 1/2... Batch 1600... Discriminator Loss: 1.2586... Generator Loss: 1.2401
Epoch 1/2... Batch 1700... Discriminator Loss: 1.1892... Generator Loss: 1.7485
Epoch 1/2... Batch 1800... Discriminator Loss: 1.1743... Generator Loss: 1.2026
Epoch 2/2... Batch 100... Discriminator Loss: 1.1407... Generator Loss: 1.2428
Epoch 2/2... Batch 200... Discriminator Loss: 1.0291... Generator Loss: 1.1471
Epoch 2/2... Batch 300... Discriminator Loss: 1.0642... Generator Loss: 1.1316
Epoch 2/2... Batch 400... Discriminator Loss: 1.0481... Generator Loss: 1.1941
Epoch 2/2... Batch 500... Discriminator Loss: 1.1296... Generator Loss: 1.5759
Epoch 2/2... Batch 600... Discriminator Loss: 1.2898... Generator Loss: 0.6084
Epoch 2/2... Batch 700... Discriminator Loss: 1.0690... Generator Loss: 1.3797
Epoch 2/2... Batch 800... Discriminator Loss: 1.0538... Generator Loss: 0.8358
Epoch 2/2... Batch 900... Discriminator Loss: 1.2350... Generator Loss: 1.2154
Epoch 2/2... Batch 1000... Discriminator Loss: 1.0991... Generator Loss: 1.7122
Epoch 2/2... Batch 1100... Discriminator Loss: 1.3594... Generator Loss: 0.5011
Epoch 2/2... Batch 1200... Discriminator Loss: 1.1585... Generator Loss: 1.7717
Epoch 2/2... Batch 1300... Discriminator Loss: 0.9566... Generator Loss: 2.0970
Epoch 2/2... Batch 1400... Discriminator Loss: 0.9640... Generator Loss: 1.2614
Epoch 2/2... Batch 1500... Discriminator Loss: 1.1896... Generator Loss: 0.7015
Epoch 2/2... Batch 1600... Discriminator Loss: 1.3620... Generator Loss: 0.5928
Epoch 2/2... Batch 1700... Discriminator Loss: 1.0766... Generator Loss: 0.8028
Epoch 2/2... Batch 1800... Discriminator Loss: 1.2242... Generator Loss: 0.6373

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [14]:
batch_size = 32
z_dim = 64
learning_rate = 0.0006
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 100... Discriminator Loss: 1.8562... Generator Loss: 0.3155
Epoch 1/1... Batch 200... Discriminator Loss: 1.2727... Generator Loss: 0.6469
Epoch 1/1... Batch 300... Discriminator Loss: 1.4726... Generator Loss: 0.4344
Epoch 1/1... Batch 400... Discriminator Loss: 0.9965... Generator Loss: 0.9550
Epoch 1/1... Batch 500... Discriminator Loss: 1.4172... Generator Loss: 0.6077
Epoch 1/1... Batch 600... Discriminator Loss: 1.3713... Generator Loss: 0.6294
Epoch 1/1... Batch 700... Discriminator Loss: 1.1276... Generator Loss: 0.7639
Epoch 1/1... Batch 800... Discriminator Loss: 1.1983... Generator Loss: 0.7535
Epoch 1/1... Batch 900... Discriminator Loss: 1.3184... Generator Loss: 0.9030
Epoch 1/1... Batch 1000... Discriminator Loss: 1.4240... Generator Loss: 0.4781
Epoch 1/1... Batch 1100... Discriminator Loss: 1.5073... Generator Loss: 1.4332
Epoch 1/1... Batch 1200... Discriminator Loss: 1.4756... Generator Loss: 0.5859
Epoch 1/1... Batch 1300... Discriminator Loss: 1.1610... Generator Loss: 0.7352
Epoch 1/1... Batch 1400... Discriminator Loss: 1.5028... Generator Loss: 0.8213
Epoch 1/1... Batch 1500... Discriminator Loss: 1.3137... Generator Loss: 0.6150
Epoch 1/1... Batch 1600... Discriminator Loss: 1.3789... Generator Loss: 0.8387
Epoch 1/1... Batch 1700... Discriminator Loss: 1.2489... Generator Loss: 0.8087
Epoch 1/1... Batch 1800... Discriminator Loss: 1.2653... Generator Loss: 1.1803
Epoch 1/1... Batch 1900... Discriminator Loss: 1.4432... Generator Loss: 0.6421
Epoch 1/1... Batch 2000... Discriminator Loss: 1.3494... Generator Loss: 0.8449
Epoch 1/1... Batch 2100... Discriminator Loss: 1.2832... Generator Loss: 0.6921
Epoch 1/1... Batch 2200... Discriminator Loss: 1.3958... Generator Loss: 0.7873
Epoch 1/1... Batch 2300... Discriminator Loss: 1.3488... Generator Loss: 0.6665
Epoch 1/1... Batch 2400... Discriminator Loss: 1.4017... Generator Loss: 0.8704
Epoch 1/1... Batch 2500... Discriminator Loss: 1.2925... Generator Loss: 0.5439
Epoch 1/1... Batch 2600... Discriminator Loss: 1.3330... Generator Loss: 0.8379
Epoch 1/1... Batch 2700... Discriminator Loss: 1.2654... Generator Loss: 0.6858
Epoch 1/1... Batch 2800... Discriminator Loss: 1.2706... Generator Loss: 0.8583
Epoch 1/1... Batch 2900... Discriminator Loss: 1.2613... Generator Loss: 0.6294
Epoch 1/1... Batch 3000... Discriminator Loss: 1.3792... Generator Loss: 0.6919
Epoch 1/1... Batch 3100... Discriminator Loss: 1.3280... Generator Loss: 0.9204
Epoch 1/1... Batch 3200... Discriminator Loss: 1.2867... Generator Loss: 1.1558
Epoch 1/1... Batch 3300... Discriminator Loss: 1.3944... Generator Loss: 0.8422
Epoch 1/1... Batch 3400... Discriminator Loss: 1.3852... Generator Loss: 0.6336
Epoch 1/1... Batch 3500... Discriminator Loss: 1.2509... Generator Loss: 0.6706
Epoch 1/1... Batch 3600... Discriminator Loss: 1.2593... Generator Loss: 0.8200
Epoch 1/1... Batch 3700... Discriminator Loss: 1.5111... Generator Loss: 0.5236
Epoch 1/1... Batch 3800... Discriminator Loss: 1.2929... Generator Loss: 0.7609
Epoch 1/1... Batch 3900... Discriminator Loss: 1.4009... Generator Loss: 0.7268
Epoch 1/1... Batch 4000... Discriminator Loss: 1.2287... Generator Loss: 0.6797
Epoch 1/1... Batch 4100... Discriminator Loss: 1.3541... Generator Loss: 0.7990
Epoch 1/1... Batch 4200... Discriminator Loss: 1.3445... Generator Loss: 0.7233
Epoch 1/1... Batch 4300... Discriminator Loss: 1.3007... Generator Loss: 0.7962
Epoch 1/1... Batch 4400... Discriminator Loss: 1.4053... Generator Loss: 1.1373
Epoch 1/1... Batch 4500... Discriminator Loss: 1.2080... Generator Loss: 0.7032
Epoch 1/1... Batch 4600... Discriminator Loss: 1.0255... Generator Loss: 0.7881
Epoch 1/1... Batch 4700... Discriminator Loss: 1.3027... Generator Loss: 0.7948
Epoch 1/1... Batch 4800... Discriminator Loss: 1.3538... Generator Loss: 0.6452
Epoch 1/1... Batch 4900... Discriminator Loss: 1.2863... Generator Loss: 0.8461
Epoch 1/1... Batch 5000... Discriminator Loss: 1.3365... Generator Loss: 0.6817
Epoch 1/1... Batch 5100... Discriminator Loss: 1.3187... Generator Loss: 0.7309
Epoch 1/1... Batch 5200... Discriminator Loss: 1.3482... Generator Loss: 0.8452
Epoch 1/1... Batch 5300... Discriminator Loss: 1.1896... Generator Loss: 1.0730
Epoch 1/1... Batch 5400... Discriminator Loss: 1.2152... Generator Loss: 0.6739
Epoch 1/1... Batch 5500... Discriminator Loss: 1.2721... Generator Loss: 0.7327
Epoch 1/1... Batch 5600... Discriminator Loss: 1.2322... Generator Loss: 1.1076
Epoch 1/1... Batch 5700... Discriminator Loss: 1.4817... Generator Loss: 1.0610
Epoch 1/1... Batch 5800... Discriminator Loss: 1.3018... Generator Loss: 0.8615
Epoch 1/1... Batch 5900... Discriminator Loss: 1.3120... Generator Loss: 0.6421
Epoch 1/1... Batch 6000... Discriminator Loss: 1.3475... Generator Loss: 0.6213
Epoch 1/1... Batch 6100... Discriminator Loss: 1.2538... Generator Loss: 0.9641
Epoch 1/1... Batch 6200... Discriminator Loss: 1.2208... Generator Loss: 0.8504
Epoch 1/1... Batch 6300... Discriminator Loss: 1.3532... Generator Loss: 0.8724

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.